{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "<i>Copyright (c) Recommenders contributors.</i>\n",
                "\n",
                "<i>Licensed under the MIT License.</i>"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Data visualization and analysis for Microsoft Academic Graph"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:41.047674Z",
                    "start_time": "2020-08-01T01:08:40.495117Z"
                }
            },
            "outputs": [],
            "source": [
                "from utils.PandasMagClass import MicrosoftAcademicGraph\n",
                "import numpy as np\n",
                "import pandas as pd\n",
                "import datetime as dt\n",
                "import matplotlib.pyplot as plt\n",
                "import matplotlib.ticker as ticker\n",
                "import seaborn as sns\n",
                "import os"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:41.051853Z",
                    "start_time": "2020-08-01T01:08:41.049580Z"
                }
            },
            "outputs": [],
            "source": [
                "root = './data_folder/raw/'\n",
                "mag = MicrosoftAcademicGraph(root)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:45.224231Z",
                    "start_time": "2020-08-01T01:08:41.053525Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>PaperId</th>\n",
                            "      <th>Rank</th>\n",
                            "      <th>Doi</th>\n",
                            "      <th>DocType</th>\n",
                            "      <th>PaperTitle</th>\n",
                            "      <th>OriginalTitle</th>\n",
                            "      <th>BookTitle</th>\n",
                            "      <th>Year</th>\n",
                            "      <th>Date</th>\n",
                            "      <th>OnlineDate</th>\n",
                            "      <th>...</th>\n",
                            "      <th>Volume</th>\n",
                            "      <th>Issue</th>\n",
                            "      <th>FirstPage</th>\n",
                            "      <th>LastPage</th>\n",
                            "      <th>ReferenceCount</th>\n",
                            "      <th>CitationCount</th>\n",
                            "      <th>EstimatedCitation</th>\n",
                            "      <th>OriginalVenue</th>\n",
                            "      <th>FamilyId</th>\n",
                            "      <th>CreatedDate</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>22085</td>\n",
                            "      <td>10.1533/9781845694586.3.194</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>detecting virus contamination in seafood</td>\n",
                            "      <td>Detecting virus contamination in seafood.</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2008</td>\n",
                            "      <td>2008-01-01 12:00:00</td>\n",
                            "      <td>NaT</td>\n",
                            "      <td>...</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>194</td>\n",
                            "      <td>211</td>\n",
                            "      <td>105</td>\n",
                            "      <td>1</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Improving Seafood Products for the Consumer</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>392414</td>\n",
                            "      <td>25423</td>\n",
                            "      <td>10.1007/978-88-470-0556-3_60</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>il lavaggio broncoalveolare bal in eta pediatrica</td>\n",
                            "      <td>Il lavaggio broncoalveolare (BAL) in età pedia...</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2007</td>\n",
                            "      <td>2007-01-01 12:00:00</td>\n",
                            "      <td>NaT</td>\n",
                            "      <td>...</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>621</td>\n",
                            "      <td>634</td>\n",
                            "      <td>35</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>442880</td>\n",
                            "      <td>24185</td>\n",
                            "      <td>10.1007/978-1-4684-5823-7_2</td>\n",
                            "      <td>Journal</td>\n",
                            "      <td>background paper functions of coronavirus glyc...</td>\n",
                            "      <td>Background paper: functions of coronavirus gly...</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>1990</td>\n",
                            "      <td>1990-01-01 12:00:00</td>\n",
                            "      <td>NaT</td>\n",
                            "      <td>...</td>\n",
                            "      <td>276</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>5</td>\n",
                            "      <td>7</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4</td>\n",
                            "      <td>4</td>\n",
                            "      <td>Advances in Experimental Medicine and Biology</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>3 rows × 24 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   PaperId   Rank                           Doi  DocType  \\\n",
                            "0   342037  22085   10.1533/9781845694586.3.194      NaN   \n",
                            "1   392414  25423  10.1007/978-88-470-0556-3_60      NaN   \n",
                            "2   442880  24185   10.1007/978-1-4684-5823-7_2  Journal   \n",
                            "\n",
                            "                                          PaperTitle  \\\n",
                            "0           detecting virus contamination in seafood   \n",
                            "1  il lavaggio broncoalveolare bal in eta pediatrica   \n",
                            "2  background paper functions of coronavirus glyc...   \n",
                            "\n",
                            "                                       OriginalTitle BookTitle  Year  \\\n",
                            "0          Detecting virus contamination in seafood.       NaN  2008   \n",
                            "1  Il lavaggio broncoalveolare (BAL) in età pedia...       NaN  2007   \n",
                            "2  Background paper: functions of coronavirus gly...       NaN  1990   \n",
                            "\n",
                            "                 Date OnlineDate  ... Volume  Issue  FirstPage  LastPage  \\\n",
                            "0 2008-01-01 12:00:00        NaT  ...    NaN    NaN        194       211   \n",
                            "1 2007-01-01 12:00:00        NaT  ...    NaN    NaN        621       634   \n",
                            "2 1990-01-01 12:00:00        NaT  ...    276    NaN          5         7   \n",
                            "\n",
                            "  ReferenceCount CitationCount EstimatedCitation  \\\n",
                            "0            105             1                 1   \n",
                            "1             35             0                 0   \n",
                            "2              1             4                 4   \n",
                            "\n",
                            "                                   OriginalVenue  FamilyId         CreatedDate  \n",
                            "0    Improving Seafood Products for the Consumer       NaN 2016-06-24 12:00:00  \n",
                            "1                                            NaN       NaN 2016-06-24 12:00:00  \n",
                            "2  Advances in Experimental Medicine and Biology       NaN 2016-06-24 12:00:00  \n",
                            "\n",
                            "[3 rows x 24 columns]"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# load MAG data: Papers\n",
                "df_papers = mag.get_data_frame('Papers')\n",
                "df_papers.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:49.253951Z",
                    "start_time": "2020-08-01T01:08:45.226138Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>PaperId</th>\n",
                            "      <th>AuthorId</th>\n",
                            "      <th>AffiliationId</th>\n",
                            "      <th>AuthorSequenceNumber</th>\n",
                            "      <th>OriginalAuthor</th>\n",
                            "      <th>OriginalAffiliation</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>2110117508</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>7</td>\n",
                            "      <td>F. S. le Guyader</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>2135466750</td>\n",
                            "      <td>71999127</td>\n",
                            "      <td>2</td>\n",
                            "      <td>R. M. Pintó</td>\n",
                            "      <td>University of Barcelona, (Spain)</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>2149630968</td>\n",
                            "      <td>71999127</td>\n",
                            "      <td>1</td>\n",
                            "      <td>A. Bosch</td>\n",
                            "      <td>University of Barcelona, (Spain)</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   PaperId    AuthorId  AffiliationId  AuthorSequenceNumber    OriginalAuthor  \\\n",
                            "0   342037  2110117508            NaN                     7  F. S. le Guyader   \n",
                            "1   342037  2135466750       71999127                     2       R. M. Pintó   \n",
                            "2   342037  2149630968       71999127                     1          A. Bosch   \n",
                            "\n",
                            "                OriginalAffiliation  \n",
                            "0                               NaN  \n",
                            "1  University of Barcelona, (Spain)  \n",
                            "2  University of Barcelona, (Spain)  "
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# load MAG data: Paper Author Affiliations\n",
                "df_paper_author_affiliations = mag.get_data_frame('PaperAuthorAffiliations')\n",
                "df_paper_author_affiliations.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:50.121113Z",
                    "start_time": "2020-08-01T01:08:49.255569Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>FieldOfStudyId</th>\n",
                            "      <th>Rank</th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>DisplayName</th>\n",
                            "      <th>MainType</th>\n",
                            "      <th>Level</th>\n",
                            "      <th>PaperCount</th>\n",
                            "      <th>PaperFamilyCount</th>\n",
                            "      <th>CitationCount</th>\n",
                            "      <th>CreatedDate</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>12843</td>\n",
                            "      <td>11873</td>\n",
                            "      <td>gravitational singularity</td>\n",
                            "      <td>Gravitational singularity</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2</td>\n",
                            "      <td>38572</td>\n",
                            "      <td>36517</td>\n",
                            "      <td>498012</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>20288</td>\n",
                            "      <td>11771</td>\n",
                            "      <td>superstring theory</td>\n",
                            "      <td>Superstring theory</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>3</td>\n",
                            "      <td>7425</td>\n",
                            "      <td>6989</td>\n",
                            "      <td>196930</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>40700</td>\n",
                            "      <td>9651</td>\n",
                            "      <td>industrial organization</td>\n",
                            "      <td>Industrial organization</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>1</td>\n",
                            "      <td>320951</td>\n",
                            "      <td>319290</td>\n",
                            "      <td>2659089</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   FieldOfStudyId   Rank             NormalizedName  \\\n",
                            "0           12843  11873  gravitational singularity   \n",
                            "1           20288  11771         superstring theory   \n",
                            "2           40700   9651    industrial organization   \n",
                            "\n",
                            "                 DisplayName MainType  Level  PaperCount  PaperFamilyCount  \\\n",
                            "0  Gravitational singularity      NaN      2       38572             36517   \n",
                            "1         Superstring theory      NaN      3        7425              6989   \n",
                            "2    Industrial organization      NaN      1      320951            319290   \n",
                            "\n",
                            "   CitationCount         CreatedDate  \n",
                            "0         498012 2016-06-24 12:00:00  \n",
                            "1         196930 2016-06-24 12:00:00  \n",
                            "2        2659089 2016-06-24 12:00:00  "
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# load MAG data: FieldOfStudy\n",
                "df_fos = mag.get_data_frame('FieldsOfStudy')\n",
                "df_fos.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:52.495839Z",
                    "start_time": "2020-08-01T01:08:50.122509Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>PaperId</th>\n",
                            "      <th>FieldOfStudyId</th>\n",
                            "      <th>Score</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>31903555</td>\n",
                            "      <td>0.368951</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>34135077</td>\n",
                            "      <td>0.548833</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>342037</td>\n",
                            "      <td>86803240</td>\n",
                            "      <td>0.364040</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   PaperId  FieldOfStudyId     Score\n",
                            "0   342037        31903555  0.368951\n",
                            "1   342037        34135077  0.548833\n",
                            "2   342037        86803240  0.364040"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# load MAG data: Paper Field Of Study\n",
                "df_paper_fos = mag.get_data_frame('PaperFieldsOfStudy')\n",
                "df_paper_fos.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:52.615951Z",
                    "start_time": "2020-08-01T01:08:52.497584Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
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                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>JournalId</th>\n",
                            "      <th>Rank</th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>DisplayName</th>\n",
                            "      <th>Issn</th>\n",
                            "      <th>Publisher</th>\n",
                            "      <th>Webpage</th>\n",
                            "      <th>PaperCount</th>\n",
                            "      <th>PaperFamilyCount</th>\n",
                            "      <th>CitationCount</th>\n",
                            "      <th>CreatedDate</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>61661</td>\n",
                            "      <td>10724</td>\n",
                            "      <td>journal of prosthodontics</td>\n",
                            "      <td>Journal of Prosthodontics</td>\n",
                            "      <td>1059-941X</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>2759</td>\n",
                            "      <td>2759</td>\n",
                            "      <td>26991</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>255146</td>\n",
                            "      <td>9259</td>\n",
                            "      <td>synthese</td>\n",
                            "      <td>Synthese</td>\n",
                            "      <td>0039-7857</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>http://www.springer.com/11229</td>\n",
                            "      <td>7074</td>\n",
                            "      <td>7074</td>\n",
                            "      <td>76660</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>260102</td>\n",
                            "      <td>12335</td>\n",
                            "      <td>bmc emergency medicine</td>\n",
                            "      <td>BMC Emergency Medicine</td>\n",
                            "      <td>1471-227X</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>556</td>\n",
                            "      <td>556</td>\n",
                            "      <td>5457</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   JournalId   Rank             NormalizedName                DisplayName  \\\n",
                            "0      61661  10724  journal of prosthodontics  Journal of Prosthodontics   \n",
                            "1     255146   9259                   synthese                   Synthese   \n",
                            "2     260102  12335     bmc emergency medicine     BMC Emergency Medicine   \n",
                            "\n",
                            "        Issn Publisher                        Webpage  PaperCount  \\\n",
                            "0  1059-941X       NaN                            NaN        2759   \n",
                            "1  0039-7857       NaN  http://www.springer.com/11229        7074   \n",
                            "2  1471-227X       NaN                            NaN         556   \n",
                            "\n",
                            "   PaperFamilyCount  CitationCount         CreatedDate  \n",
                            "0              2759          26991 2016-06-24 12:00:00  \n",
                            "1              7074          76660 2016-06-24 12:00:00  \n",
                            "2               556           5457 2016-06-24 12:00:00  "
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# load MAG data: Journals\n",
                "df_journals = mag.get_data_frame('Journals')\n",
                "df_journals.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:52.751589Z",
                    "start_time": "2020-08-01T01:08:52.618124Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>AffiliationId</th>\n",
                            "      <th>Rank</th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>DisplayName</th>\n",
                            "      <th>GridId</th>\n",
                            "      <th>OfficialPage</th>\n",
                            "      <th>WikiPage</th>\n",
                            "      <th>PaperCount</th>\n",
                            "      <th>PaperFamilyCount</th>\n",
                            "      <th>CitationCount</th>\n",
                            "      <th>Latitude</th>\n",
                            "      <th>Longitude</th>\n",
                            "      <th>CreatedDate</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>9507</td>\n",
                            "      <td>11625</td>\n",
                            "      <td>sangji university</td>\n",
                            "      <td>Sangji University</td>\n",
                            "      <td>grid.412417.5</td>\n",
                            "      <td>http://www.sangji.ac.kr/</td>\n",
                            "      <td>http://en.wikipedia.org/wiki/Sangji_University</td>\n",
                            "      <td>1479</td>\n",
                            "      <td>1469</td>\n",
                            "      <td>13387</td>\n",
                            "      <td>37.369946</td>\n",
                            "      <td>127.928612</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>15855</td>\n",
                            "      <td>12646</td>\n",
                            "      <td>manchester institute of innovation research</td>\n",
                            "      <td>Manchester Institute of Innovation Research</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>http://en.wikipedia.org/wiki/Manchester_Instit...</td>\n",
                            "      <td>364</td>\n",
                            "      <td>356</td>\n",
                            "      <td>9278</td>\n",
                            "      <td>53.467999</td>\n",
                            "      <td>-2.236000</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>19722</td>\n",
                            "      <td>11477</td>\n",
                            "      <td>ateneo de manila university</td>\n",
                            "      <td>Ateneo de Manila University</td>\n",
                            "      <td>grid.443223.0</td>\n",
                            "      <td>http://www.ateneo.edu/</td>\n",
                            "      <td>http://en.wikipedia.org/wiki/Ateneo_de_Manila_...</td>\n",
                            "      <td>1877</td>\n",
                            "      <td>1864</td>\n",
                            "      <td>10674</td>\n",
                            "      <td>14.638890</td>\n",
                            "      <td>121.077782</td>\n",
                            "      <td>2016-06-24 12:00:00</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   AffiliationId   Rank                               NormalizedName  \\\n",
                            "0           9507  11625                            sangji university   \n",
                            "1          15855  12646  manchester institute of innovation research   \n",
                            "2          19722  11477                  ateneo de manila university   \n",
                            "\n",
                            "                                   DisplayName         GridId  \\\n",
                            "0                            Sangji University  grid.412417.5   \n",
                            "1  Manchester Institute of Innovation Research            NaN   \n",
                            "2                  Ateneo de Manila University  grid.443223.0   \n",
                            "\n",
                            "               OfficialPage  \\\n",
                            "0  http://www.sangji.ac.kr/   \n",
                            "1                       NaN   \n",
                            "2    http://www.ateneo.edu/   \n",
                            "\n",
                            "                                            WikiPage  PaperCount  \\\n",
                            "0     http://en.wikipedia.org/wiki/Sangji_University        1479   \n",
                            "1  http://en.wikipedia.org/wiki/Manchester_Instit...         364   \n",
                            "2  http://en.wikipedia.org/wiki/Ateneo_de_Manila_...        1877   \n",
                            "\n",
                            "   PaperFamilyCount  CitationCount   Latitude   Longitude         CreatedDate  \n",
                            "0              1469          13387  37.369946  127.928612 2016-06-24 12:00:00  \n",
                            "1               356           9278  53.467999   -2.236000 2016-06-24 12:00:00  \n",
                            "2              1864          10674  14.638890  121.077782 2016-06-24 12:00:00  "
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# load MAG data: Affiliations\n",
                "df_affiliations = mag.get_data_frame('Affiliations')\n",
                "df_affiliations.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:53.166766Z",
                    "start_time": "2020-08-01T01:08:52.753490Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>PaperCount</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>7378</th>\n",
                            "      <td>medrxiv</td>\n",
                            "      <td>3681</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5383</th>\n",
                            "      <td>plos one</td>\n",
                            "      <td>1977</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2525</th>\n",
                            "      <td>journal of virology</td>\n",
                            "      <td>1664</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5828</th>\n",
                            "      <td>biorxiv</td>\n",
                            "      <td>1348</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5046</th>\n",
                            "      <td>emerging infectious diseases</td>\n",
                            "      <td>1007</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1250</th>\n",
                            "      <td>surgical endoscopy and other interventional te...</td>\n",
                            "      <td>909</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>174</th>\n",
                            "      <td>virology</td>\n",
                            "      <td>882</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5243</th>\n",
                            "      <td>scientific reports</td>\n",
                            "      <td>871</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5140</th>\n",
                            "      <td>bmj</td>\n",
                            "      <td>786</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1324</th>\n",
                            "      <td>the lancet</td>\n",
                            "      <td>749</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                                         NormalizedName  PaperCount\n",
                            "7378                                            medrxiv        3681\n",
                            "5383                                           plos one        1977\n",
                            "2525                                journal of virology        1664\n",
                            "5828                                            biorxiv        1348\n",
                            "5046                       emerging infectious diseases        1007\n",
                            "1250  surgical endoscopy and other interventional te...         909\n",
                            "174                                            virology         882\n",
                            "5243                                 scientific reports         871\n",
                            "5140                                                bmj         786\n",
                            "1324                                         the lancet         749"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# top K journals\n",
                "\n",
                "# - papers with journals\n",
                "paper_with_journals = df_papers[df_papers['JournalId'].notnull()]\n",
                "\n",
                "# - join journals to get journal name\n",
                "paper_journals = pd.merge(paper_with_journals, df_journals, on ='JournalId', how = 'inner')[['JournalId', 'NormalizedName', 'PaperId']]\n",
                "\n",
                "# - group by journals and count papers\n",
                "journals_stats = paper_journals.groupby(['JournalId', 'NormalizedName']).size().to_frame('PaperCount').reset_index().sort_values(by=['PaperCount'], ascending=False)\n",
                "\n",
                "# - get top 10 journals\n",
                "journals_stats_top_10 = journals_stats.nlargest(10, 'PaperCount')[['NormalizedName', 'PaperCount']]\n",
                "\n",
                "journals_stats_top_10"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:53.367561Z",
                    "start_time": "2020-08-01T01:08:53.168248Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 864x432 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# top K journals: figure\n",
                "plt.figure(figsize=(12, 6))\n",
                "ax_topk_journals = plt.subplot()\n",
                "ax_topk_journals = sns.barplot(x=\"PaperCount\", y=\"NormalizedName\", data=journals_stats_top_10)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:53.549748Z",
                    "start_time": "2020-08-01T01:08:53.368995Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "6.155347014184458"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# team size average\n",
                "\n",
                "# - group by paperId to get team size for each paper\n",
                "paper_team_size = df_paper_author_affiliations.groupby(['PaperId']).size().to_frame('TeamSize').reset_index()\n",
                "\n",
                "# - get average team size for all papers\n",
                "paper_team_size_avg = paper_team_size['TeamSize'].mean()\n",
                "\n",
                "paper_team_size_avg"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:53.580105Z",
                    "start_time": "2020-08-01T01:08:53.551184Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>TeamSize</th>\n",
                            "      <th>PaperCount</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>(0, 2]</td>\n",
                            "      <td>29792</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>(2, 4]</td>\n",
                            "      <td>27138</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>(4, 6]</td>\n",
                            "      <td>21485</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>(6, 8]</td>\n",
                            "      <td>14402</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>(8, 10]</td>\n",
                            "      <td>8910</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>(10, 12]</td>\n",
                            "      <td>5120</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>(12, 14]</td>\n",
                            "      <td>2988</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>(14, 16]</td>\n",
                            "      <td>1774</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>(16, 18]</td>\n",
                            "      <td>1152</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>(18, 20]</td>\n",
                            "      <td>775</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>10</th>\n",
                            "      <td>(20, 5465]</td>\n",
                            "      <td>2295</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "      TeamSize  PaperCount\n",
                            "0       (0, 2]       29792\n",
                            "1       (2, 4]       27138\n",
                            "2       (4, 6]       21485\n",
                            "3       (6, 8]       14402\n",
                            "4      (8, 10]        8910\n",
                            "5     (10, 12]        5120\n",
                            "6     (12, 14]        2988\n",
                            "7     (14, 16]        1774\n",
                            "8     (16, 18]        1152\n",
                            "9     (18, 20]         775\n",
                            "10  (20, 5465]        2295"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# team size distribution\n",
                "\n",
                "# - generate paper count per team size \n",
                "team_size_distribution = paper_team_size.groupby(['TeamSize']).size().to_frame('PaperCount').reset_index()\n",
                "\n",
                "# - set team size intervals\n",
                "#bins = [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 5465]\n",
                "bins = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 5465]\n",
                "\n",
                "# - generate paper count per team size intervals\n",
                "team_size_range = paper_team_size.groupby(pd.cut(paper_team_size['TeamSize'], bins=bins)).size().to_frame('PaperCount').reset_index()\n",
                "\n",
                "team_size_range"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:53.802119Z",
                    "start_time": "2020-08-01T01:08:53.581497Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 864x432 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# team size distribution: figure\n",
                "plt.figure(figsize=(12, 6))\n",
                "ax_team_size_distribution = plt.subplot()\n",
                "ax_team_size_distribution = sns.barplot(x=\"TeamSize\", y=\"PaperCount\", data=team_size_range)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:53.820002Z",
                    "start_time": "2020-08-01T01:08:53.803551Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>AffiliationId</th>\n",
                            "      <th>Name</th>\n",
                            "      <th>Region</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>721619</td>\n",
                            "      <td>Benemérita Universidad Autónoma de Puebla</td>\n",
                            "      <td>Mexico</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>6615443</td>\n",
                            "      <td>Hydro One</td>\n",
                            "      <td>Canada</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>8086503</td>\n",
                            "      <td>Sir Salimullah Medical College</td>\n",
                            "      <td>Bangladesh</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   AffiliationId                                       Name      Region\n",
                            "0         721619  Benemérita Universidad Autónoma de Puebla      Mexico\n",
                            "1        6615443                                  Hydro One      Canada\n",
                            "2        8086503             Sir Salimullah Medical College  Bangladesh"
                        ]
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# top K geolocation by past 20 year: load data\n",
                "df_affiliation_regions = pd.read_table(os.path.join(root, 'AffiliationRegions.txt'), low_memory=False, names=('AffiliationId', 'Name', 'Region'))\n",
                "df_affiliation_regions.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:55.134905Z",
                    "start_time": "2020-08-01T01:08:53.821323Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Region</th>\n",
                            "      <th>Year</th>\n",
                            "      <th>PaperCount</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>956</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2017</td>\n",
                            "      <td>2355</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>954</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2015</td>\n",
                            "      <td>2340</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>955</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2016</td>\n",
                            "      <td>2229</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>953</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2014</td>\n",
                            "      <td>2107</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>958</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2019</td>\n",
                            "      <td>2044</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>957</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2018</td>\n",
                            "      <td>1994</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>952</th>\n",
                            "      <td>United States</td>\n",
                            "      <td>2013</td>\n",
                            "      <td>1870</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>254</th>\n",
                            "      <td>EU</td>\n",
                            "      <td>2019</td>\n",
                            "      <td>1806</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>252</th>\n",
                            "      <td>EU</td>\n",
                            "      <td>2017</td>\n",
                            "      <td>1733</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>250</th>\n",
                            "      <td>EU</td>\n",
                            "      <td>2015</td>\n",
                            "      <td>1704</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "            Region  Year  PaperCount\n",
                            "956  United States  2017        2355\n",
                            "954  United States  2015        2340\n",
                            "955  United States  2016        2229\n",
                            "953  United States  2014        2107\n",
                            "958  United States  2019        2044\n",
                            "957  United States  2018        1994\n",
                            "952  United States  2013        1870\n",
                            "254             EU  2019        1806\n",
                            "252             EU  2017        1733\n",
                            "250             EU  2015        1704"
                        ]
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# top K geolocation by past 20 year\n",
                "\n",
                "# - filter papers publish year\n",
                "papers = df_papers[(df_papers.Year >= 2000) & (df_papers.Year <= 2019)]\n",
                "\n",
                "# - join papers with paper author affiliation\n",
                "papers_with_affi = pd.merge(papers, df_paper_author_affiliations, on ='PaperId', how = 'inner')[['PaperId', 'Year', 'AffiliationId']]\n",
                "\n",
                "# - keep only non-empty and distinct rows\n",
                "papers_with_affi_distinct = papers_with_affi[papers_with_affi['AffiliationId'].notnull()].drop_duplicates()\n",
                "\n",
                "# - join to get affiliation region\n",
                "papers_affi_region = pd.merge(df_affiliation_regions, papers_with_affi_distinct, on ='AffiliationId', how = 'inner')[['PaperId', 'Year', 'AffiliationId', 'Region']]\n",
                "\n",
                "# - eliminate double count when authors in one paper are from different affiliations but in the same country\n",
                "papers_year_region = papers_affi_region.groupby(['PaperId', 'Year', 'Region']).size().reset_index()[['PaperId', 'Year', 'Region']]\n",
                "\n",
                "# - group to get paper count per region and year\n",
                "region_year_papercount = papers_year_region.groupby(['Region', 'Year']).size().to_frame('PaperCount').reset_index().sort_values(by=['PaperCount'], ascending=False)\n",
                "\n",
                "# - get paper count per region\n",
                "region_papercount = region_year_papercount.groupby(['Region'])['PaperCount'].sum().reset_index()\n",
                "\n",
                "# - get region top paper count\n",
                "top_region_papercount = region_papercount.nlargest(5, 'PaperCount')['Region'].tolist()\n",
                "\n",
                "# - filter top countries on papers count per year and country\n",
                "top_region_year_papercount = region_year_papercount[region_year_papercount['Region'].isin(set(top_region_papercount))]\n",
                "\n",
                "top_region_year_papercount.head(10)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:32:55.746396Z",
                    "start_time": "2020-08-01T01:32:55.354129Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 864x432 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# top K geolocation by past 20 year: figure\n",
                "plt.figure(figsize=(12, 6))\n",
                "ax_topk_geolocation = plt.subplot()\n",
                "ax_topk_geolocation = sns.lineplot(x=\"Year\", y=\"PaperCount\", hue=\"Region\", style=\"Region\", markers=['o','s','v','d', '^'], hue_order=top_region_papercount, dashes=False, data=top_region_year_papercount)\n",
                "ax_topk_geolocation.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:.0f}'.format(x)))\n",
                "ax_topk_geolocation.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:55.757547Z",
                    "start_time": "2020-08-01T01:08:55.503499Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>PaperCntSum</th>\n",
                            "      <th>Percentage</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>medicine</td>\n",
                            "      <td>63225</td>\n",
                            "      <td>54.51%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>36513</td>\n",
                            "      <td>31.48%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>others</td>\n",
                            "      <td>8008</td>\n",
                            "      <td>6.90%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>3936</td>\n",
                            "      <td>3.39%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>computer science</td>\n",
                            "      <td>2219</td>\n",
                            "      <td>1.91%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>psychology</td>\n",
                            "      <td>2078</td>\n",
                            "      <td>1.79%</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     NormalizedName  PaperCntSum Percentage\n",
                            "0          medicine        63225     54.51%\n",
                            "1           biology        36513     31.48%\n",
                            "2            others         8008      6.90%\n",
                            "3         chemistry         3936      3.39%\n",
                            "4  computer science         2219      1.91%\n",
                            "5        psychology         2078      1.79%"
                        ]
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# L0 top Field Of Study distribution\n",
                "L0_TopN = 5\n",
                "\n",
                "L0_fos = df_fos.loc[df_fos['Level'] == 0]\n",
                "paper_L0_fos = pd.merge(L0_fos, df_paper_fos, on = 'FieldOfStudyId', how='inner')\n",
                "L0_fos_stats = paper_L0_fos.groupby('NormalizedName').agg({'PaperId':['count']})\n",
                "L0_fos_stats.columns = ['PaperCnt']\n",
                "L0_fos_stats.sort_values(by=['PaperCnt'], inplace = True, ascending=False)\n",
                "L0_fos_stats_sorted = L0_fos_stats.reset_index()\n",
                "\n",
                "L0_fos_stats_sorted.loc[L0_fos_stats_sorted.index >= L0_TopN, 'NormalizedName'] = 'others'\n",
                "L0_fos_stats_topN = L0_fos_stats_sorted.groupby('NormalizedName').agg({'PaperCnt':['sum']})\n",
                "L0_fos_stats_topN.columns = ['PaperCntSum']\n",
                "L0_fos_stats_topN.sort_values(by=['PaperCntSum'], inplace = True, ascending=False)\n",
                "L0_fos_stats_topN = L0_fos_stats_topN.reset_index()\n",
                "L0_fos_stats_topN['Percentage'] = L0_fos_stats_topN.PaperCntSum / L0_fos_stats_topN.PaperCntSum.sum()\n",
                "L0_fos_stats_topN['Percentage'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in L0_fos_stats_topN['Percentage']], index = L0_fos_stats_topN.index)\n",
                "\n",
                "L0_fos_stats_topN"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:55.850426Z",
                    "start_time": "2020-08-01T01:08:55.758963Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 1080x576 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# L0 top Field Of Study distribution : figure\n",
                "domains = list(L0_fos_stats_topN['NormalizedName'])\n",
                "PaperCnt_domain = list(L0_fos_stats_topN['PaperCntSum'])\n",
                "fig = plt.figure(figsize = (15, 8)) \n",
                "plt.pie(PaperCnt_domain, labels = domains) \n",
                "plt.title(\"No. of papers in different domains\") \n",
                "plt.show() "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:56.173633Z",
                    "start_time": "2020-08-01T01:08:55.851732Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>PaperCnt</th>\n",
                            "      <th>Percentage</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>virology</td>\n",
                            "      <td>29567</td>\n",
                            "      <td>26.28%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>immunology</td>\n",
                            "      <td>15954</td>\n",
                            "      <td>14.18%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>surgery</td>\n",
                            "      <td>15667</td>\n",
                            "      <td>13.92%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>internal medicine</td>\n",
                            "      <td>12045</td>\n",
                            "      <td>10.70%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>intensive care medicine</td>\n",
                            "      <td>10624</td>\n",
                            "      <td>9.44%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>molecular biology</td>\n",
                            "      <td>7268</td>\n",
                            "      <td>6.46%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>pathology</td>\n",
                            "      <td>6611</td>\n",
                            "      <td>5.88%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>genetics</td>\n",
                            "      <td>5231</td>\n",
                            "      <td>4.65%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>general surgery</td>\n",
                            "      <td>4963</td>\n",
                            "      <td>4.41%</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>anesthesia</td>\n",
                            "      <td>4594</td>\n",
                            "      <td>4.08%</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "            NormalizedName  PaperCnt Percentage\n",
                            "0                 virology     29567     26.28%\n",
                            "1               immunology     15954     14.18%\n",
                            "2                  surgery     15667     13.92%\n",
                            "3        internal medicine     12045     10.70%\n",
                            "4  intensive care medicine     10624      9.44%\n",
                            "5        molecular biology      7268      6.46%\n",
                            "6                pathology      6611      5.88%\n",
                            "7                 genetics      5231      4.65%\n",
                            "8          general surgery      4963      4.41%\n",
                            "9               anesthesia      4594      4.08%"
                        ]
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# L1 top Field Of Study distribution\n",
                "L1_TopN = 10\n",
                "\n",
                "L1_fos = df_fos.loc[df_fos['Level'] == 1]\n",
                "paper_L1_fos = pd.merge(L1_fos, df_paper_fos, on = 'FieldOfStudyId', how='inner')\n",
                "L1_fos_stats = paper_L1_fos.groupby('NormalizedName').agg({'PaperId':['count']})\n",
                "L1_fos_stats.columns = ['PaperCnt']\n",
                "L1_fos_stats.sort_values(by=['PaperCnt'], inplace = True, ascending=False)\n",
                "L1_fos_stats_sorted = L1_fos_stats.reset_index()\n",
                "\n",
                "L1_fos_stats_topN1 = L1_fos_stats_sorted.nlargest(L1_TopN, 'PaperCnt')\n",
                "L1_fos_stats_topN1['Percentage'] = L1_fos_stats_topN1.PaperCnt / L1_fos_stats_topN1.PaperCnt.sum()\n",
                "L1_fos_stats_topN1['Percentage'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in L1_fos_stats_topN1['Percentage']], index = L1_fos_stats_topN1.index)\n",
                "\n",
                "L1_fos_stats_topN1"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:56.281720Z",
                    "start_time": "2020-08-01T01:08:56.175039Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 1080x576 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# L1 top Field Of Study distribution : figure\n",
                "domains = list(L1_fos_stats_topN1['NormalizedName'])\n",
                "PaperCnt_domain = list(L1_fos_stats_topN1['PaperCnt'])\n",
                "fig = plt.figure(figsize = (15, 8))\n",
                "plt.pie(PaperCnt_domain, labels = domains) \n",
                "plt.title(\"No. of papers in different subdomains\") \n",
                "plt.show() "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:56.790264Z",
                    "start_time": "2020-08-01T01:08:56.283115Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>Year</th>\n",
                            "      <th>PaperCountSum</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2000</td>\n",
                            "      <td>262</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2001</td>\n",
                            "      <td>314</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2002</td>\n",
                            "      <td>243</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2003</td>\n",
                            "      <td>478</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2004</td>\n",
                            "      <td>784</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2005</td>\n",
                            "      <td>942</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2006</td>\n",
                            "      <td>1208</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2007</td>\n",
                            "      <td>1037</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2008</td>\n",
                            "      <td>1233</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2009</td>\n",
                            "      <td>1369</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>10</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2010</td>\n",
                            "      <td>1344</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>11</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2011</td>\n",
                            "      <td>1400</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>12</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2012</td>\n",
                            "      <td>1531</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>13</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2013</td>\n",
                            "      <td>1634</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>14</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2014</td>\n",
                            "      <td>1788</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>15</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2015</td>\n",
                            "      <td>2019</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>16</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2016</td>\n",
                            "      <td>2062</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>17</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2017</td>\n",
                            "      <td>2088</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>18</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2018</td>\n",
                            "      <td>2121</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>19</th>\n",
                            "      <td>biology</td>\n",
                            "      <td>2019</td>\n",
                            "      <td>2321</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>20</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2000</td>\n",
                            "      <td>10</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>21</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2001</td>\n",
                            "      <td>6</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>22</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2002</td>\n",
                            "      <td>9</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>23</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2003</td>\n",
                            "      <td>19</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>24</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2004</td>\n",
                            "      <td>61</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2005</td>\n",
                            "      <td>90</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>26</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2006</td>\n",
                            "      <td>110</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>27</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2007</td>\n",
                            "      <td>123</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>28</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2008</td>\n",
                            "      <td>179</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>29</th>\n",
                            "      <td>chemistry</td>\n",
                            "      <td>2009</td>\n",
                            "      <td>147</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   NormalizedName  Year  PaperCountSum\n",
                            "0         biology  2000            262\n",
                            "1         biology  2001            314\n",
                            "2         biology  2002            243\n",
                            "3         biology  2003            478\n",
                            "4         biology  2004            784\n",
                            "5         biology  2005            942\n",
                            "6         biology  2006           1208\n",
                            "7         biology  2007           1037\n",
                            "8         biology  2008           1233\n",
                            "9         biology  2009           1369\n",
                            "10        biology  2010           1344\n",
                            "11        biology  2011           1400\n",
                            "12        biology  2012           1531\n",
                            "13        biology  2013           1634\n",
                            "14        biology  2014           1788\n",
                            "15        biology  2015           2019\n",
                            "16        biology  2016           2062\n",
                            "17        biology  2017           2088\n",
                            "18        biology  2018           2121\n",
                            "19        biology  2019           2321\n",
                            "20      chemistry  2000             10\n",
                            "21      chemistry  2001              6\n",
                            "22      chemistry  2002              9\n",
                            "23      chemistry  2003             19\n",
                            "24      chemistry  2004             61\n",
                            "25      chemistry  2005             90\n",
                            "26      chemistry  2006            110\n",
                            "27      chemistry  2007            123\n",
                            "28      chemistry  2008            179\n",
                            "29      chemistry  2009            147"
                        ]
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# L0 top Field Of Study distribution by year\n",
                "L0_topN = 5\n",
                "\n",
                "L0_fos = df_fos.loc[df_fos['Level'] == 0]\n",
                "paper_L0_fos = pd.merge(L0_fos, df_paper_fos, on = 'FieldOfStudyId', how='inner')[['NormalizedName', 'Level', 'PaperId']]\n",
                "papers = df_papers[(df_papers.Year >= 2000) & (df_papers.Year <= 2019)]\n",
                "paper_year_L0_fos = pd.merge(paper_L0_fos, papers, on = 'PaperId', how='inner')[['NormalizedName', 'Level', 'PaperId', 'Year']]\n",
                "fos_L0_year_papercount = paper_year_L0_fos.groupby(['NormalizedName', 'Year']).size().to_frame('PaperCount').reset_index().sort_values(by=['PaperCount'], ascending=False)\n",
                "\n",
                "fos_L0_papercount = fos_L0_year_papercount.groupby(['NormalizedName'])['PaperCount'].sum().reset_index()\n",
                "top_fos_L0_papercount = fos_L0_papercount.nlargest(L0_topN, 'PaperCount')['NormalizedName'].tolist()\n",
                "\n",
                "fos_L0_year_papercount.loc[~fos_L0_year_papercount['NormalizedName'].isin(set(top_fos_L0_papercount)), 'NormalizedName'] = 'others'\n",
                "fos_L0_year_topN = fos_L0_year_papercount.groupby(['NormalizedName', 'Year']).agg({'PaperCount':['sum']}).reset_index()\n",
                "fos_L0_year_topN.columns = ['NormalizedName','Year','PaperCountSum']\n",
                "\n",
                "fos_L0_year_topN.head(30)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:33:10.670719Z",
                    "start_time": "2020-08-01T01:33:10.168162Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 864x432 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# L0 top Field Of Study distribution by year: figure\n",
                "plt.figure(figsize=(12, 6))\n",
                "top_fos_L0_papercount_order = top_fos_L0_papercount.copy()\n",
                "top_fos_L0_papercount_order.append('others')\n",
                "ax_topk_fos_L0 = plt.subplot()\n",
                "ax_topk_fos_L0 = sns.lineplot(x=\"Year\", y=\"PaperCountSum\", hue=\"NormalizedName\", style=\"NormalizedName\", markers=['o','s','v','d', '^', 'h'], hue_order=top_fos_L0_papercount_order, dashes=False, data=fos_L0_year_topN)\n",
                "ax_topk_fos_L0.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:.0f}'.format(x)))\n",
                "ax_topk_fos_L0.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:08:57.857108Z",
                    "start_time": "2020-08-01T01:08:57.225489Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>NormalizedName</th>\n",
                            "      <th>Year</th>\n",
                            "      <th>PaperCount</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>3282</th>\n",
                            "      <td>surgery</td>\n",
                            "      <td>2015</td>\n",
                            "      <td>1734</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3456</th>\n",
                            "      <td>virology</td>\n",
                            "      <td>2016</td>\n",
                            "      <td>1671</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3455</th>\n",
                            "      <td>virology</td>\n",
                            "      <td>2015</td>\n",
                            "      <td>1636</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     NormalizedName  Year  PaperCount\n",
                            "3282        surgery  2015        1734\n",
                            "3456       virology  2016        1671\n",
                            "3455       virology  2015        1636"
                        ]
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# L1 top Field Of Study distribution by year\n",
                "L1_topN = 10\n",
                "\n",
                "L1_fos = df_fos.loc[df_fos['Level'] == 1]\n",
                "paper_L1_fos = pd.merge(L1_fos, df_paper_fos, on = 'FieldOfStudyId', how='inner')[['NormalizedName', 'Level', 'PaperId']]\n",
                "papers = df_papers[(df_papers.Year >= 2000) & (df_papers.Year <= 2019)]\n",
                "paper_year_L1_fos = pd.merge(paper_L1_fos, papers, on = 'PaperId', how='inner')[['NormalizedName', 'Level', 'PaperId', 'Year']]\n",
                "fos_L1_year_papercount = paper_year_L1_fos.groupby(['NormalizedName', 'Year']).size().to_frame('PaperCount').reset_index().sort_values(by=['PaperCount'], ascending=False)\n",
                "\n",
                "fos_L1_papercount = fos_L1_year_papercount.groupby(['NormalizedName'])['PaperCount'].sum().reset_index()\n",
                "top_fos_L1_papercount = fos_L1_papercount.nlargest(L1_topN, 'PaperCount')['NormalizedName'].tolist()\n",
                "top_fos_L1_year_papercount = fos_L1_year_papercount[fos_L1_year_papercount['NormalizedName'].isin(set(top_fos_L1_papercount))]\n",
                "\n",
                "top_fos_L1_year_papercount.head(3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2020-08-01T01:33:26.863101Z",
                    "start_time": "2020-08-01T01:33:25.960414Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 864x432 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# L1 top Field Of Study distribution by year: figure\n",
                "plt.figure(figsize=(12, 6))\n",
                "ax_topk_fos_L1 = plt.subplot()\n",
                "ax_topk_fos_L1 = sns.lineplot(x=\"Year\", y=\"PaperCount\", hue=\"NormalizedName\", style=\"NormalizedName\", markers=['o','s','v','d','^','h','<','.','p','*'], hue_order=top_fos_L1_papercount, dashes=False, data=top_fos_L1_year_papercount)\n",
                "ax_topk_fos_L1.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:.0f}'.format(x)))\n",
                "ax_topk_fos_L1.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python (kdd tutorial)",
            "language": "python",
            "name": "kdd_tutorial_2020"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.10"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}